Skip to main content

Incorporating Nelder-Mead Simplex as an Accelerating Operator to Improve the Performance of Metaheuristics in Nonlinear System Identification

  • Living reference work entry
  • First Online:
Handbook of Formal Optimization

Abstract

This chapter explores the use of Nelder-Mead simplex (NMS) as an accelerating operator to improve the performance of metaheuristic algorithms, which are commonly used for solving complex optimization problems. NMS is a search method that forms a simplex of points, iteratively transforming it to find the optimal solution. The incorporation of NMS in metaheuristic algorithms can significantly enhance the convergence speed and solution quality. The solutions in each iteration are modified by several operations of reflection, contraction, and expansion to enhance the algorithm. A case study of nonlinear system identification in structural dynamics is presented. The problem is defined to estimate the parameters of the modified Bouc-Wen model of a magnetorheological damper (MRD). The results demonstrate that the incorporation of NMS accelerates and improves the performance of each algorithm. The proposed methods offer a promising solution for enhancing the performance of metaheuristic algorithms in solving complex optimization problems.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Institutional subscriptions

Similar content being viewed by others

References

  • Abdel-Basset M, Mohamed R, Mirjalili S (2021) A novel whale optimization algorithm integrated with Nelder–Mead simplex for multi-objective optimization problems. Knowl-Based Syst 212:106619. https://doi.org/10.1016/j.knosys.2020.106619

    Article  Google Scholar 

  • Abualigah L, Diabat A (2020) A comprehensive survey of the grasshopper optimization algorithm: results, variants, and applications. Neural Comput Applic 32:15533–15556. https://doi.org/10.1007/s00521-020-04789-8

    Article  Google Scholar 

  • Abualigah L, Elaziz MA, Sumari P et al (2022) Black hole algorithm: a comprehensive survey. Appl Intell 52:11892–11915. https://doi.org/10.1007/s10489-021-02980-5

    Article  Google Scholar 

  • Ashtari P, Karami R, Farahmand-Tabar S (2021) Optimum geometrical pattern and design of real-size diagrid structures using accelerated fuzzy-genetic algorithm with bilinear membership function. Appl Soft Comput 110:107646. https://doi.org/10.1016/j.asoc.2021.107646

    Article  Google Scholar 

  • Atashpaz-Gargari E, Lucas C (2007) Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. IEEE Congr Evol Comput CEC 007:4661–4667. https://doi.org/10.1109/CEC.2007.4425083

    Article  Google Scholar 

  • Azar BF, Veladi H, Talatahari S, Raeesi F (2020) Optimal design of magnetorheological damper based on tuning Bouc-Wen model parameters using hybrid algorithms. KSCE J Civ Eng 24:867–878

    Article  Google Scholar 

  • Back T, Schwefel HP (1993) An overview of evolutionary algorithms for parameter optimization. Evol Comput 1(1):1–23

    Article  Google Scholar 

  • Bai XX, Cai FL, Chen P (2019) Resistor-capacitor (RC) operator-based hysteresis model for magneto-rheological (MR) dampers. Mech Syst Signal Process 117:157–169

    Article  Google Scholar 

  • Bouc R (1967) Forced vibration of mechanical systems with hysteresis. In: Proceedings of the fourth conference on nonlinear oscillation, Prague, September

    Google Scholar 

  • Coello Coello C.A. (2002) Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: a survey of the state of the art Comput Meth Appl Mech Eng. 191:1245–1287.

    Google Scholar 

  • Charalampakis AE, Dimou CK (2010) Identification of Bouc-Wen hysteretic systems using particle swarm optimization. Comput Struct 88:1197–1205

    Article  Google Scholar 

  • Chen H, Jiao S, Heidari AA, Wang M, Chen X, Zhao X (2019) An opposition-based sine cosine approach with local search for parameter estimation of photovoltaic models. Energy Convers Manag 195:927–942. https://doi.org/10.1016/j.enconman.2019.05.057

    Article  Google Scholar 

  • Choi SB, Lee HS, Park YP (2001) A hysteresis model for the field-dependent damping force of a magneto-rheological damper. J Sound Vib 245(2):375–383

    Article  Google Scholar 

  • Dyke SJ, Spencer BF, Sain MK, Carlson JD (1996) Modeling and control of magneto-rheological dampers for seismic response reduction. Smart Mater Struct 5(5):565–575

    Article  Google Scholar 

  • Fakhouri HN, Hudaib A, Sleit A (2020) Hybrid particle swarm optimization with sine cosine algorithm and nelder–mead simplex for solving engineering design problems. Arab J Sci Eng 45:3091–3109. https://doi.org/10.1007/s13369-019-04285-9

    Article  Google Scholar 

  • Farahmand-Tabar S, Ashtari P (2020) Simultaneous size and topology optimization of 3D outrigger-braced tall buildings with inclined belt truss using genetic algorithm. Struct Des Tall Special Build 29(13):e1776. https://doi.org/10.1002/tal.1776

    Article  Google Scholar 

  • Farahmand-Tabar S (2023) Genetic algorithm and accelerating fuzzification for optimum sizing and topology design of real-size tall building systems. In: Dey N (eds.) Applied genetic algorithm and its variants. Springer Tracts in Nature-Inspired Computing. Springer, Singapore. https://doi.org/10.1007/978-981-99-3428-7_9

  • Farahmand-Tabar S (2024) Frequency-based optimization of truss dome structures using ant colony optimization (ACOR) with multi-trail pheromone memory. In: Dey N (eds.) Applications of ant colony optimization and its variants. Springer tracts in nature-inspired computing. Springer, Singapore. (In Press)

    Google Scholar 

  • Farahmand-Tabar S, Shirgir S (2024a) Antlion-facing ant colony optimization in parameter identification of the MR damper as a semi-active control device. In: Dey N (eds.) Applications of Ant Colony Optimization and Its Variants. Springer tracts in nature-inspired computing. Springer, Singapore. (In Press)

    Google Scholar 

  • Farahmand-Tabar S, Shirgir S (2024b) Opposed pheromone ant colony optimization for property identification of nonlinear structures. In: Dey N (eds.) Applications of ant colony optimization and its variants. Springer tracts in nature-inspired computing. Springer, Singapore. (In Press)

    Google Scholar 

  • Farahmand-Tabar S, Babaei M (2023) Memory-assisted adaptive multi-verse optimizer and its application in structural shape and size optimization. Soft Comput. https://doi.org/10.1007/s00500-023-08349-9

  • Gavin HP, Zaicenco A (2007) Performance and reliability of semi-active equipment isolation. J Sound Vib 306(1–2):74–90

    Article  Google Scholar 

  • Graczykowski C, PawÅ‚owski P (2017) Exact physical model of magneto-rheological damper. Appl Math Model 47:400–424

    Article  MathSciNet  MATH  Google Scholar 

  • Guo A, Xu Y, Wu B (2002) Seismic reliability analysis of hysteretic structure with viscoelastic dampers. Eng Struct 24(3):373–383

    Article  Google Scholar 

  • Hadidi A, Azar BF, Rafiee A (2016) Reliability-based design of semi-rigidly connected base-isolated buildings subjected to stochastic near-fault excitations. Earthq Struct 11(4):701–721

    Article  Google Scholar 

  • Hadidi A, Azar BF, Shirgir S (2019) Reliability assessment of semi-active control of structures with MR damper. Earthq Struct 17(2):131–141

    Google Scholar 

  • Hatamlou A (2013) Black hole: a new heuristic optimization approach for data clustering. Inf Sci 222:175–184

    Article  MathSciNet  Google Scholar 

  • Hong S, Wereley N, Choi Y, Choi S (2008) Analytical and experimental validation of a nondimensional Bingham model for mixed-mode magneto-rheological dampers. J Sound Vib 312(3):399–417

    Article  Google Scholar 

  • Ikhouane F, Manosa V, Rodellar J (2007) Dynamic properties of the hysteretic Bouc-Wen model. Syst Control Lett 56:197–205

    Article  MathSciNet  MATH  Google Scholar 

  • Ismail M, Ikhouane F, Rodellar J (2009) The hysteresis Bouc-Wen model, a survey. Arch Comput Methods Eng 16:161–188

    Article  MATH  Google Scholar 

  • Kwok N, Ha Q, Nguyen T, Li J, Samali B (2006) A novel hysteretic model for magneto-rheological fluid dampers and parameter identification using particle swarm optimization. Sensors Actuators A Phys 132(2):441–451

    Article  Google Scholar 

  • Kwok NM, Ha QP, Nguye MT, Li J, Samali B (2007) Bouc-Wen model parameter identification for a MR fluid damper using computationally efficient GA. ISA Trans 46(2):167–179

    Article  Google Scholar 

  • Liu P, Liu H, Teng J, Cao T (2006) Parameters identification for smart dampers based on simulated annealing and genetic algorithm. In: Proceedings of the IEEE international conference on mechatronics and automation, Henan, June

    Google Scholar 

  • Mrabet E, Guedri M, Ichchou M, Ghanmi S (2015) Stochastic structural and reliability based optimization of tuned mass damper. Mech Syst Signal Process 60:437–451

    Article  Google Scholar 

  • Rakotondrabe M (2011) Bouc-Wen modeling and inverse multiplicative structure to compensate hysteresis nonlinearity in piezoelectric actuators. IEEE Trans Autom Sci Eng 8(2):428–431

    Article  Google Scholar 

  • Saremi S, Mirjalili S, Lewis A (2017) Grasshopper optimisation algorithm: theory and application. Adv Eng Softw 105:30–47

    Article  Google Scholar 

  • Shirgir S, Farahmand-Tabar S, Aghabeigi P (2023) Optimum design of real-size reinforced concrete bridge via charged system search algorithm trained by nelder-mead simplex, Expert systems with applications, 121815. https://doi.org/10.1016/j.eswa.2023.121815

  • Song J, Kiureghian AD (2006) Generalized Bouc–Wen model for highly asymmetric hysteresis. J Eng Mech 132(6):610–618

    Article  Google Scholar 

  • Spencer B Jr, Sain M, Kantor J, Montemagno C (1992) Probabilistic stability measures for controlled structures subject to real parameter uncertainties. Smart Mater Struct 1(4):294

    Article  Google Scholar 

  • Spencer B Jr, Dyke S, Sain M, Carlson J (1997) Phenomenological model for magneto-rheological dampers. J Eng Mech 123(3):230–238

    Article  Google Scholar 

  • Spall J.C. (2005) Introduction to stochastic search and optimization: estimation, simulation, and control, vol. 65, John Wiley & Sons.

    Google Scholar 

  • Sun H, Lus H, Betti H (2013) Identification of structural models using a modified Artificial Bee Colony algorithm. Comput Struct 116:59–74

    Article  Google Scholar 

  • Talatahari S, Rahbari NM (2015) Enriched imperialist competitive algorithm for system identification of magneto-rheological dampers. Mech Syst Signal Process 62–63:506–516

    Article  Google Scholar 

  • Talatahari S, Kaveh A, Rahbari NM (2012) Parameter identification of Bouc-Wen model for MR fluid dampers using adaptive charged system search optimization. Mech Sci Technol 26(8):1–12

    Article  Google Scholar 

  • Wen YK (1976) Method for random vibration of hysteretic systems. J Eng Mech 102(2):249–263

    Google Scholar 

  • Wen YK (1980) Equivalent linearization for hysteretic systems under random excitation. J Appl Mech 47(1):150–154

    Article  MATH  Google Scholar 

  • Wen YK (1989) Methods of random vibration for inelastic structures. Appl Mech Rev 42(2):39–52

    Article  Google Scholar 

  • Yang G, Spencer BF, Carlson JD, Sain MK (2002) Large-scale MR fluid dampers: modeling and dynamic performance considerations. Eng Struct 24(3):309–323

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Salar Farahmand-Tabar .

Editor information

Editors and Affiliations

Section Editor information

Rights and permissions

Reprints and permissions

Copyright information

© 2023 Springer Nature Singapore Pte Ltd.

About this entry

Check for updates. Verify currency and authenticity via CrossMark

Cite this entry

Farahmand-Tabar, S., Shirgir, S. (2023). Incorporating Nelder-Mead Simplex as an Accelerating Operator to Improve the Performance of Metaheuristics in Nonlinear System Identification. In: Kulkarni, A.J., Gandomi, A.H. (eds) Handbook of Formal Optimization. Springer, Singapore. https://doi.org/10.1007/978-981-19-8851-6_39-1

Download citation

  • DOI: https://doi.org/10.1007/978-981-19-8851-6_39-1

  • Received:

  • Accepted:

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-8851-6

  • Online ISBN: 978-981-19-8851-6

  • eBook Packages: Springer Reference Intelligent Technologies and RoboticsReference Module Computer Science and Engineering

Publish with us

Policies and ethics